It's like the difference between theory and praxis, identifying causal relationships vs cultivating observational information. One could argue that, in terms of the history of science, the former is a Popperian stance while the latter is a Baconian framework. A Popperian theory is one that is fundamentally driven by theoretical and mathematical assumptions, e.g., as in physics. Baconian science, on the other hand, is primarily observational in nature and attempts to develop theory from the cumulation of initially purely observational information. The biological sciences or the modern field of informatics are representative of this latter point of view.
The literature on causality and the power of experimental design and random controlled trials (RCTs) vs less powerful observational studies that may be randomly sampled and nationally projectable but are not otherwise considered experimental designs is huge and quite contentious. Judea Pearl is probably the most prominent scholar on the "causal" side as his work is widely read and cited but others include James Heckman (the Nobel Laureate) and Donald Rubin (prominent Harvard Bayesian). Wrt leveraging weaker observational studies for causal relationships, it seems that everyone has a point of view about this but there is little in the way of consensus that I'm aware of. The "causal" guys tend to be kind of rigidly orthodox in their insistence on RCTs as the gold standard for all scientific endeavor while the observational guys tend to sort of cower under this withering glare as "nonscientists."
So, are you interested in determining causal relationships? Are the empirical results that are driving the observed correlation an artifact of the experimental data or does your "theory" need to be adjusted? In other words and to Christoph Hanck's point, is the relationship captured by this correlation an artifact of some statistical endogeneity (variously defined but Wooldridge's definition is best and simply refers to undesired association between the model residual and the predictors or X variables). If so, then economists' would prescribe developing an instrumental variable or set of IVs to control for this endogeneity. IV's are a big subject that's too broad for this little post.
If your study or project does not require you to make rigorous statements and judgments about causality, then you are on more observational ground where determining associations among the information is the rule and causality, depending on your POV, takes a back seat.
It sounds like the study you reference on "Dow Jones Women at the Wheel" properly falls in the observational category. Therefore, it is on much weaker interpretive ground. Specifically to your point, and without even reading the report, the extracted quote that the observed difference in proportions, "demonstrat(es) the value that having more females can potentially bring to a management team" is not substantiated with any supporting evidence. One is left wondering whether or not this difference matters. Unfortunately and based solely on the quote, there is nothing to support this conclusion beyond eyeballing the percentages. For some people, this includes many marketers as well as those with a political or ideological bias towards confirmation of one opinion or another, this visual "evidence" is enough. There may be additional information in the report that goes further in substantiating this claim, but it's not evident from the quote. As I indicated, I haven't drilled into the report to confirm or disconfirm this and I don't want to.
This gets to the final point worth making and has to do with the intended audience for this analysis. The study you cite falls into the domain of market research. Market researchers are way downstream both in reputation and importance from the vaunted frameworks of Popperian vs Baconian science. The biggest problem is that it is a field populated to a huge degree by the technically illiterate and innumerate who shrink at the use of geek jargon (even words like "correlation" can be suspect) and make every effort to scrub their reports of verbiage which the lowest common denominator of unskill cannot sustain. In large part, this is due to the emphasis from the c-suite level on down in many companies on promoting "great communicators," people who can orate effectively but who amount to empty suits. For confirmation of this just consider the total market caps of technically literate companies (e.g., Google, Amazon, Apple and FB, about 800 billion dollars) vs the market caps of the large agency holding companies (technically illiterate, e.g., WPP, Publicis, Omnicon, Interbrand, about 80 billion dollars). The literate entities have a 10x advantage in valuation over the illiterate ones. In other words, the smoke and mirror agencies aren't fooling anyone, least of all Wall St.
That said, this doesn't mean that all marketers are sleazebags who are only in the business to blow smoke and sell mirrors. There are those in this business who are highly technically strong and rigorous in their methodology and reportage. It's just that they are regrettably in the minority.